The goal of this assignment is to first familiarize you with heart rate, and respiration data and their preprocessing. The second part explores how to analyze interpersonal coordination of these signals.
These are the questions you need to be able to answer at the end of the assignment (aka that you need to submit as part of the portfolio)
How do you preprocess heart rate and respiration data? Describe the process. If any data needs to be excluded, list the excluded data and motivate the exclusion.
Do you observe interpersonal coordination in heart rate and respiration? Describe your control baseline, the method used to quantify coordination, and the statistical models used to infer whether coordination was higher than in the baseline. Report the results of the models.
Do you observe differences in coordination between conditions? Report the models and results.
Is respiration coordination a likely driver of heart rate coordination? Describe how you would test for it. Bonus points if you actually run the tests and report methods and results.
N.B. to give you a bit more data I included data from last year (Study1) and from your class (Study2). Note that synchronouns and turn-taking are the same across both studies, but the third condition is different: last year it was self-paced joint reading; this year it was the tv-series conversation. So you might want to exclude the self-paced reading (but, up to you!)
Can you eye-ball which condition if any displays more physiological coordination?
Does this tell you more than just eyeballing the plots?
# Set working directory
setwd("~/OneDrive - Aarhus universitet/AU-Cognitive Science/3rd Semester/Experimental Methods 3/Exercise/Assignments/Assignment_3NEW")
# Load relevant packages
library(ggplot2); library(groupdata2); library(dplyr); library(crqa); library(stringr); library(readr)
pakke ‘ggplot2’ blev bygget under R version 3.3.2pakke ‘groupdata2’ blev bygget under R version 3.3.2pakke ‘dplyr’ blev bygget under R version 3.3.2
Vedhæfter pakke: ‘dplyr’
De følgende objekter er maskerede fra ‘package:stats’:
filter, lag
De følgende objekter er maskerede fra ‘package:base’:
intersect, setdiff, setequal, union
Indlæser krævet pakke: Matrix
pakke ‘Matrix’ blev bygget under R version 3.3.2Indlæser krævet pakke: tseriesChaos
Indlæser krævet pakke: deSolve
pakke ‘deSolve’ blev bygget under R version 3.3.2Indlæser krævet pakke: fields
pakke ‘fields’ blev bygget under R version 3.3.2Indlæser krævet pakke: spam
pakke ‘spam’ blev bygget under R version 3.3.2Indlæser krævet pakke: dotCall64
Indlæser krævet pakke: grid
kunne ikke tildele RegisteredNativeSymbol for toeplitz til toeplitz da toeplitz allerede er defineret i navnerummet ‘spam’Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Vedhæfter pakke: ‘spam’
De følgende objekter er maskerede fra ‘package:base’:
backsolve, forwardsolve
Indlæser krævet pakke: maps
pakke ‘maps’ blev bygget under R version 3.3.2Indlæser krævet pakke: plot3D
pakke ‘plot3D’ blev bygget under R version 3.3.2Indlæser krævet pakke: pracma
pakke ‘pracma’ blev bygget under R version 3.3.2
Vedhæfter pakke: ‘pracma’
Det følgende objekt er maskeret fra ‘package:deSolve’:
rk4
De følgende objekter er maskerede fra ‘package:Matrix’:
expm, lu, tril, triu
pakke ‘stringr’ blev bygget under R version 3.3.2pakke ‘readr’ blev bygget under R version 3.3.2
# Load in datafile
Synch_data=read.csv("Data/Study2_G5_T1_Synchronous.csv")
Turn_data=read.csv("Data/Study2_G5_T2_TurnTaking.csv")
Conv_data=read.csv("Data/Study2_G5_T3_Conversation.csv")
# To create plots for respiration
ggplot(Conv_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Turn_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Synch_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
# To create plots for heart rate
ggplot(Conv_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Turn_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Synch_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
# To downsample data
Conv_data = Conv_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
package ‘bindrcpp’ was built under R version 3.3.2
Synch_data = Synch_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
Turn_data = Turn_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
# To remove outliers
removeOuts <- function(ts,threshold){
ts[ts > (mean(ts,na.rm=T) + (threshold*sd(ts,na.rm=T))) | ts < (mean(ts,na.rm=T) - (threshold*sd(ts,na.rm=T)))] = mean(ts,na.rm=T)
return(ts)}
threshold=2.5
Conv_data$HR1=removeOuts(Conv_data$HR1,threshold)
Conv_data$HR2=removeOuts(Conv_data$HR2,threshold)
Turn_data$HR1=removeOuts(Turn_data$HR1, threshold)
Turn_data$HR2=removeOuts(Turn_data$HR2, threshold)
Synch_data$HR1=removeOuts(Synch_data$HR1, threshold)
Synch_data$HR2=removeOuts(Synch_data$HR2, threshold)
Conv_data$Resp1=removeOuts(Conv_data$Resp1,threshold)
Conv_data$Resp2=removeOuts(Conv_data$Resp2,threshold)
Turn_data$Resp1=removeOuts(Turn_data$Resp1, threshold)
Turn_data$Resp2=removeOuts(Turn_data$Resp2, threshold)
Synch_data$Resp1=removeOuts(Synch_data$Resp1, threshold)
Synch_data$Resp2=removeOuts(Synch_data$Resp2, threshold)
# To scale data
Conv_data$Resp1S=scale(Conv_data$Resp1)
Conv_data$Resp2S=scale(Conv_data$Resp2)
Conv_data$HR1S=scale(Conv_data$HR1)
Conv_data$HR2S=scale(Conv_data$HR2)
Turn_data$Resp1S=scale(Turn_data$Resp1)
Turn_data$Resp2S=scale(Turn_data$Resp2)
Turn_data$HR1S=scale(Turn_data$HR1)
Turn_data$HR2S=scale(Turn_data$HR2)
Synch_data$Resp1S=scale(Synch_data$Resp1)
Synch_data$Resp2S=scale(Synch_data$Resp2)
Synch_data$HR1S=scale(Synch_data$HR1)
Synch_data$HR2S=scale(Synch_data$HR2)
# To create HR plot for sclaed data
plot1 = ggplot(Conv_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot2 = ggplot(Turn_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot3 = ggplot(Synch_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
# To make one grid with all HR plots
gridExtra::grid.arrange(plot1, plot2, plot3)
plot4 = ggplot(Conv_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot5 = ggplot(Turn_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot6 = ggplot(Synch_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
# To make one grid with all Respiration plots
gridExtra::grid.arrange(plot4, plot5, plot6)
# To perform CRQA
par = list(lgM = 50, steps = seq(1, 6, 1), radiusspan = 100, radiussample = 40, normalize = 0, rescale = 0, mindiagline = 2, minvertline = 2, tw = 0, whiteline = FALSE, recpt = FALSE, fnnpercent = 10, typeami = "mindip")
# To create a loop to get paramters
files = list(Conv_data, Synch_data, Turn_data)
Dimension_Resp = NULL
Radius_Resp = NULL
Delay_Resp = NULL
Dimension_HR = NULL
Radius_HR = NULL
Delay_HR = NULL
n=1
for (file in files) {
ans_Resp = try(optimizeParam(file$Resp1S, file$Resp2S, par, min.rec = 2, max.rec = 8))
ans_HR = try(optimizeParam(file$HR1S, file$HR2S, par, min.rec = 2, max.rec = 8))
if (length(ans_Resp) > 1) {
Dimension_Resp[n] = ans_Resp$emddim
Radius_Resp[n] = ans_Resp$radius
Delay_Resp[n] = ans_Resp$delay
}
else {
Dimension_Resp[n] = NA
Radius_Resp[n] = NA
Delay_Resp[n] = NA
}
if (length(ans_HR) > 1) {
Dimension_HR[n] = ans_HR$emddim
Radius_HR[n] = ans_HR$radius
Delay_HR[n] = ans_HR$delay
}
else {
Dimension_HR[n] = NA
Radius_HR[n] = NA
Delay_HR[n] = NA
}
n=n+1
}
Optimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample size
parameters = data.frame(Dimension_Resp, Radius_Resp, Delay_Resp, Dimension_HR, Radius_HR, Delay_HR)
mean(parameters$Dimension_Resp, na.rm = TRUE) # 2
[1] 2
mean(parameters$Delay_Resp, na.rm = TRUE) # 28
[1] 28
mean(parameters$Radius_Resp, na.rm = TRUE) # 0.378
[1] 0.3778026
Results=crqa (Synch_data$Resp1S, Synch_data$Resp2S, delay=28, embed=2, radius= 0.378,normalize=0,rescale=0,mindiagline = 2,minvertline = 2)
# To plot RQA
RP=Results$RP
RP = matrix(as.numeric(RP), nrow = ncol(RP))
cols = c("white","blue4")
image(RP, xlab = "", ylab = "", col = cols)
# To explore lags of coordination
Profile=drpdfromts(Synch_data$Resp1S, Synch_data$Resp2S,datatype = 'continuous',ws=50,radius=0.443)
timecourse = round( seq(-5000,5000,100)/1000, digit = 1)
maxlag = Profile$maxlag/1000
profile = Profile$profile*100
Prof=data.frame(profile)
ggplot(Prof, aes(timecourse,profile))+geom_line()+ geom_vline(xintercept = timecourse[maxlag], colour='red')
# To create a filelist
filelist = list.files(path = "Data", pattern = ".csv")
# To create a function that can preprocess the data
preprocessing = function(data) {
data = data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
data$HR1=removeOuts(data$HR1,threshold)
data$HR2=removeOuts(data$HR2,threshold)
data$Resp1=removeOuts(data$Resp1,threshold)
data$Resp2=removeOuts(data$Resp2,threshold)
data$Resp1=scale(data$Resp1)
data$Resp2=scale(data$Resp2)
data$HR1=scale(data$HR1)
data$HR2=scale(data$HR2)
return(data)
}
# To create a loop to preproces all the data and create plots
Final_data = data.frame()
n=1
for (file in filelist) {
data=read_csv(paste0("Data/",file))
Datafile=preprocessing(data)
Datafile$filename = filelist[n]
ans_Resp = try(optimizeParam(Datafile$Resp1, Datafile$Resp2, par, min.rec = 2, max.rec = 8))
ans_HR = try(optimizeParam(Datafile$HR1, Datafile$HR2, par, min.rec = 2, max.rec = 8))
if (length(ans_Resp) > 1) {
Datafile$Dimension_Resp = ans_Resp$emddim
Datafile$Radius_Resp = ans_Resp$radius
Datafile$Delay_Resp = ans_Resp$delay
}
else {
Datafile$Dimension_Resp = NA
Datafile$Radius_Resp = NA
Datafile$Delay_Resp = NA
}
if (length(ans_HR) > 1) {
Datafile$Dimension_HR = ans_HR$emddim
Datafile$Radius_HR = ans_HR$radius
Datafile$Delay_HR = ans_HR$delay
}
else {
Datafile$Dimension_HR = NA
Datafile$Radius_HR = NA
Datafile$Delay_HR = NA
}
Final_data = rbind(Final_data, Datafile)
Resp_plot= ggplot(Datafile, aes(x=time, y= Resp1)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2), color = "blue")
HR_plot = ggplot(Datafile, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
final_plot=gridExtra::grid.arrange(Resp_plot, HR_plot)
ggsave(paste0(file, ".png"), plot = final_plot, path = "Plot")
n=n+1
}
Parsed with column specification:
cols(
time = col_double(),
Resp1 = col_integer(),
Resp2 = col_double(),
ECG1 = col_double(),
ECG2 = col_double(),
HR1 = col_double(),
HR2 = col_double()
)
number of columns of result is not a multiple of vector length (arg 1)31102 parsing failures.
row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 8110 Resp1 no trailing characters .99939 'Data/Study1_G1_T1_Synchronous.csv' file 2 8112 Resp1 no trailing characters .99939 'Data/Study1_G1_T1_Synchronous.csv' row 3 8113 Resp1 no trailing characters .99512 'Data/Study1_G1_T1_Synchronous.csv' col 4 8114 Resp1 no trailing characters .99207 'Data/Study1_G1_T1_Synchronous.csv' expected 5 8115 Resp1 no trailing characters .99237 'Data/Study1_G1_T1_Synchronous.csv'
... ................. ... ............................................................................... ........ ............................................................................... ...... ............................................................................... .... ............................................................................... ... ............................................................................... ... ............................................................................... ........ ...............................................................................
See problems(...) for more details.
Error : NA/NaN/Inf in foreign function call (arg 1)
Error : NA/NaN/Inf in foreign function call (arg 1)
Error : NA/NaN/Inf in foreign function call (arg 1)
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Error : NA/NaN/Inf in foreign function call (arg 1)
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# Based on plots we choose to remove the following data:
# Study1_G1_T1, Study1_G1_T2, Study1_G2_T1, Study1_G2_T2
Final_data=Final_data[Final_data$filename != "Study1_G1_T1_Synchronous.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G1_T2_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T1_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T2_Synchronous.csv", ]
# To get column with study number
for (file in 1:nrow(Final_data)){
if (grepl("Study1", Final_data$filename[file])){
Final_data$Study[file] = 1}
if (grepl("Study2", Final_data$filename[file])){
Final_data$Study[file] = 2}
}
Unknown or uninitialised column: 'Study'.
# To get group number
Final_data$Group=regmatches(Final_data$filename, regexpr("[G].*[0-9]", Final_data$filename))
Final_data$Group = gsub("[G, _, T]", "", Final_data$Group)
Final_data$Group=substr(Final_data$Group, 1, nchar(Final_data$Group)-1)
# To get trial number
Final_data$Trial=regmatches(Final_data$filename, regexpr("[T].*[0-9]", Final_data$filename))
Final_data$Trial = gsub("[T]", "", Final_data$Trial)
# To get condition
Final_data = Final_data %>% group_by(filename) %>% mutate(Condition = gsub('.{4}$', '', strsplit(filename, "_")[[1]][4]))
# To write data to a csv file
write.csv(Final_data, file = "Final_data.csv")
# To get mean parameters for CRQA
mean(Final_data$Dimension_Resp, na.rm = TRUE) # 3.20 = 3
[1] 3.197319
mean(Final_data$Radius_Resp, na.rm = TRUE) # 0.614
[1] 0.6142882
mean(Final_data$Delay_Resp, na.rm = TRUE) # 29.46 = 29
[1] 29.46358
mean(Final_data$Dimension_HR, na.rm = TRUE) # 11.95 = 12
[1] 11.94974
mean(Final_data$Radius_HR, na.rm = TRUE) # 1.633
[1] 1.633311
mean(Final_data$Delay_HR, na.rm = TRUE) # 29.89 = 30
[1] 29.88559
# To create a function that can perform CRQA for each file
CRQA = function(data) {
RespCRQA = crqa(data$Resp1, data$Resp2, delay = 29, embed = 3,
radius = 0.8, normalize = 0, rescale = 0,
mindiagline = 2, minvertline = 2)
RespCRQA$RP = NULL # Remove large matrix
RespCRQA = as_tibble(RespCRQA) %>%
mutate(type = 'Resp')
HrCRQA = crqa(data$HR1, data$HR2, delay = 30, embed = 12,
radius = 2.6, normalize = 0, rescale = 0,
mindiagline = 2, minvertline = 2)
HrCRQA$RP = NULL # Remove large matrix
HrCRQA = as_tibble(HrCRQA) %>%
mutate(type = 'HR')
return(rbind(RespCRQA, HrCRQA))
}
# To use the function
crqa_results = Final_data %>%
dplyr::select(-c(.groups)) %>% # Remove .groups (not needed right?)
dplyr::group_by(filename) %>% # Group by each file
dplyr::do(CRQA(.)) %>% # Do CRQA on each group (file)
dplyr::ungroup() # Ungroup (prob. not needed, didn't check)
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Final_data2 = merge(Final_data, crqa_results, by = "filename")
# To write the data to a csv file
write.csv(Final_data2, file = "Final_data2.csv")
# To create a file list
filelist2 = list.files(path = "PreprocessedData", pattern = ".csv")
# To loop through each file, shuffle the data and perform CRQA on the shuffled data
CRQA_shuffled = data.frame()
n=1
for (file in filelist2) {
d=read_csv(paste0("PreprocessedData/",file))
d$HR1 = sample(d$HR1)
d$HR2 = sample(d$HR2)
d$Resp1 = sample(d$Resp1)
d$Resp2 = sample(d$Resp2)
data=CRQA(d)
data$filename = filelist2[n]
CRQA_shuffled=rbind(data, CRQA_shuffled)
n=n+1
}
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
# To remove last 4 characters in filename (replication of .csv)
CRQA_shuffled$filename = substr(CRQA_shuffled$filename,1,nchar(CRQA_shuffled$filename)-4)
# To specify type of CRQA
crqa_results$CRQA = "Normal"
CRQA_shuffled$CRQA = "Shuffled Control"
# To combine dataset
CRQA_data = rbind(crqa_results, CRQA_shuffled)
CRQA_Resp=subset(CRQA_data[CRQA_data$type == "Resp",])
CRQA_HR=subset(CRQA_data[CRQA_data$type == "HR",])
# Statistically compare relevant CRQA indexes in normal data and shuffled controls
m1=lm(L ~ CRQA, CRQA_HR)
summary(m1)
Call:
lm(formula = L ~ CRQA, data = CRQA_HR)
Residuals:
Min 1Q Median 3Q Max
-3.7342 -0.7254 -0.0071 0.0160 12.3456
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.9489 0.4933 14.088 < 2e-16 ***
CRQAShuffled Control -4.9362 0.6976 -7.076 4.56e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.515 on 50 degrees of freedom
Multiple R-squared: 0.5004, Adjusted R-squared: 0.4904
F-statistic: 50.07 on 1 and 50 DF, p-value: 4.562e-09
m2 = lm(L~ CRQA, CRQA_Resp)
summary(m2)
Call:
lm(formula = L ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-0.61101 -0.01296 -0.00243 0.02301 0.93517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.00521 0.06078 65.90 <2e-16 ***
CRQAShuffled Control -1.95728 0.08595 -22.77 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3099 on 50 degrees of freedom
Multiple R-squared: 0.9121, Adjusted R-squared: 0.9103
F-statistic: 518.6 on 1 and 50 DF, p-value: < 2.2e-16
m3=lm(RR ~ CRQA, CRQA_HR)
summary(m3)
Call:
lm(formula = RR ~ CRQA, data = CRQA_HR)
Residuals:
Min 1Q Median 3Q Max
-3.4174 -1.4206 -0.5285 0.8121 9.9431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8194 0.5205 7.338 1.78e-09 ***
CRQAShuffled Control -2.3049 0.7361 -3.131 0.00291 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.654 on 50 degrees of freedom
Multiple R-squared: 0.1639, Adjusted R-squared: 0.1472
F-statistic: 9.804 on 1 and 50 DF, p-value: 0.002907
m4 = lm(RR~ CRQA, CRQA_Resp)
summary(m4)
Call:
lm(formula = RR ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-1.7477 -0.5310 -0.1684 0.4924 2.2263
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.9838 0.1582 31.51 <2e-16 ***
CRQAShuffled Control -0.4116 0.2237 -1.84 0.0717 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8065 on 50 degrees of freedom
Multiple R-squared: 0.06343, Adjusted R-squared: 0.0447
F-statistic: 3.386 on 1 and 50 DF, p-value: 0.07167
m5 = lm (ENTR ~ CRQA_HR)
Fejl i eval(expr, envir, enclos) : objekt 'ENTR' blev ikke fundet
summary(m6)
Call:
lm(formula = RR ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-1.7477 -0.6804 -0.2512 0.5225 5.8528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.98379 0.19922 25.016 <2e-16 ***
CRQAShuffled Control -0.41161 0.28174 -1.461 0.145
CRQASurrogate 0.05509 0.20524 0.268 0.788
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.016 on 473 degrees of freedom
Multiple R-squared: 0.01085, Adjusted R-squared: 0.00667
F-statistic: 2.595 on 2 and 473 DF, p-value: 0.07572
summary(m_ENTR_resp)
Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of
freedom [lmerMod]
Formula: ENTR ~ Condition + (1 | Study) + (1 + Trial | Group)
Data: Finaldata_Resp
REML criterion at convergence: -111832.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.1216 -0.8903 0.1167 0.6473 1.7808
Random effects:
Groups Name Variance Std.Dev. Corr
Group (Intercept) 4.996e-02 0.223510537
Trial 1.793e-02 0.133884757 -0.91
Study (Intercept) 3.288e-12 0.000001813
Residual 8.553e-03 0.092484166
Number of obs: 58240, groups: Group, 10; Study, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.855845 0.031530 7.000000 58.86 1.56e-10 ***
ConditionSelfPaced 0.288444 0.002866 58217.000000 100.66 < 2e-16 ***
ConditionSynchronous 0.015790 0.001514 58227.000000 10.43 < 2e-16 ***
ConditionTurnTaking -0.037968 0.001514 58229.000000 -25.09 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CndtSP CndtnS
CndtnSlfPcd -0.025
CndtnSynchr -0.027 0.322
CndtnTrnTkn -0.029 0.499 0.555